Mathematical Modelling of Health & Safety Related Flows Future Direction

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1 Mathematical Modelling of Health & Safety Related Flows Future Direction Stefan Ledin Health & Safety Laboratory, UK

2 Content Where is modelling used? Real Life Scenarios Flows of Interest Evolution in Mathematical Modelling Turbulence Modelling Combustion Modelling Radiation Modelling Quality and Trust Future

3 Applications of Models Basic Research Consequence modelling to support COMAH Safety Cases Land Use Planning applications Incident investigations Risk Assessments Simplified Approaches Quantified Risk Assessments (QRA)

4 Examples : HSL Airflows through human airways Atmospheric dispersion modelling LNG dispersion / source terms Blast waves Fluid structure interaction (FSI) Gas leaks in enclosed spaces Gas dispersion in gas turbine enclosures Hydrogen Safety Natural ventilation on offshore platforms Smoke movement

5 Real Life Scenarios 1 Buncefield: Aerial photograph of the Buncefield incident Buncefield: Northgate Building, located to the West of the HOSL site

6 Real Life Scenarios 2 BLEVE: 2 ton LPG tank Didcot Power Station: Turbine Hall

7 Real Life Scenarios 3 Burner based on a Rolls Royce Tay can combustor, operating at P > 4.0 MPa High pressure jet of combustion products impacting on a flat plate (pre-test)

8 Real Life Scenarios 4 Family of offshore installations in the North Sea Velocity measurements in one of the process modules. Note the congestion

9 Drum Filling N.B. There are no experimental data available for this scenario. It is a proof-of-concept exercise

10 Flows of Interest Dispersion Biologically active substances Flammable substances Toxic substances Combustion Deflagration Explosions Fires Detonation, Deflagration to Detonation Transition (DDT) Ignition Cryogenic liquids Supercritical fluids Free surface flows Free or impacting highmomentum jet Combusting Non-combusting Nano-particle laden flows Occupational Health Explosion Fluid-Structure Interaction CFD and FEA Two-phase/multi-phase flows Phase transition

11 Challenges 1 Theoretical Understanding Lack of experimental data Challenges Computer Resources Model Inadequacies

12 Challenges 2 DDT Deposition Deflagration Ignition Phase transition Two-phase and multi-phase flows And combinations of the above

13 Progression Increase in Computational Effort Required Parabolic 3D, steady-state 2D axisymmetric, steady-state 3D, transient Increasingly Realistic Representation of the Flow Physics

14 Types of Models Empirical correlations derived from a large number of experiments Simple models may involve solving a set of ODEs for mass, momentum, etc. Statistical models CFD realistic representation of the flow, but still broadly based on empirical data Or Direct Numerical Simulation which means that all relevant length and time scales are resolved No Modelling!!!

15 Turbulence Modelling k-ε model Launder, Spalding, Harlow, (1967-ish) k-ω Wilcox (1985?) Menter (1992) LES Schumann (1975) RSTM Hanjalic and Launder (1972, 1976) Launder, Reece and Rodi (1975)

16 Evolution Turbulence (1) 1989 Two-equation Eddy Viscosity models Variations on the k-ε model theme Variations on the k-ω model theme Second moment closures (rarely) Reynolds stress transport models Algebraic Reynolds stress transport model 1999 Two-equation Eddy Viscosity models Variations on the k-ε model theme Variations on the k-ω model theme Second moment closures (rarely) Reynolds stress transport models Algebraic Reynolds stress transport model

17 Evolution Turbulence (2) 2009 Two-equation Eddy Viscosity models Variations on the k-ε model theme Variations on the k-ω model theme Large Eddy Simulations (LES) Standard/Dynamic Sub-Grid Scale model Detached Eddy Simulation (DES) Scale Adaptive Simulation (SAS) Second moment closures (very rarely) Reynolds stress transport models Algebraic Reynolds stress transport model

18 Combustion Modelling 1 Eddy BreakUp model (P) Spalding (1972) Eddy Dissipation Concept Model (NP) Magnussen and Hjertager (1976) Laminar Flamelets Non-Premixed Bilger and others (late 1970s) Laminar Flamelets Premixed Moss, Bray and Libby and others (late 1970s) Definitions NP: Non-Premixed, P: Premixed, pp: Partially Premixed

19 Combustion Modelling 2 Flame speed correlations (P) Bradley and others (late 1970s) Transported PDF (Detailed/Reduced Chemical Kinetics) (NP) Pope, Jones and Kollmann, and others (mid-1980s) Transported PDF (Detailed/Reduced Chemical Kinetics) (P) Pope and others (late 1990s) Definitions NP: Non-Premixed, P: Premixed, pp: Partially Premixed

20 Evolution Combustion (1) 1989 Eddy Break-Up/Eddy Dissipation models (P/NP) Laminar flamelets with prescribed PDF (P/PN) Conditional Moment Closure (NP) Reduced kinetics with transported PDF (NP) 1999 Eddy Break-Up/Eddy Dissipation models (P/NP) Flame speed correlations (P) Laminar flamelets with prescribed PDF (P/NP) Conditional Moment Closure (NP) Reduced kinetics with transported PDF (P/NP) Definitions NP: Non-Premixed, P: Premixed, pp: Partially Premixed

21 Evolution Combustion (2) 2009 Eddy Break-Up/Eddy Dissipation models (P/NP) Flame speed correlations (P) Zimont model (pp) Detailed kinetics with transported PDF (P/NP) Conditional Moment Closure [CMC] (NP) The combustion models used in academia are usually more advanced than those used routinely in industry Definitions NP: Non-Premixed, P: Premixed, pp: Partially Premixed

22 Evolution Radiation (1) 1989 Optically thin / Optically thick assumption P1 Four-flux and six-flux model Discrete Ordinate model Discrete Transfer model 1999 Optically thin / Optically thick assumption P1 Four-flux and six-flux model Discrete Ordinate model Discrete Transfer model

23 Evolution Radiation (2) 2009 Discrete Ordinate model Discrete Transfer model Monte Carlo Optically thin / Optically thick assumption P1 Four-flux and six-flux model With grey gas, mixed grey gas or narrowband model

24 Quality and Trust Verification Models Right equations Right terms Coding Validation Application of appropriate mathematical tools Include all important physical processes Right models Degree of uncertainty Uncertainties in the input Uncertainties in the modelling Best Practice Guidance Generic Application specific Training Academic (MSc/PhD?) Specific training on using the mathematical tools Proper documentation of the simulations Assumptions Parameter settings, mesh Sensitivity analysis Mesh resolution Time step Ray/photon dependence

25 Future Direction 1 Turbulence models Two-equation turbulence models, such as the k-ω and the k-ε models will find continued extensive use LES/DES/SAS will become more widely used, especially for fire modelling Reynolds stress models (and algebraic Reynolds stress models) may be used to model specific scenarios Combustion models Eddy Break-Up model and Eddy Dissipation Concept models will still be used Laminar flamelets with prescribed or transported PDF are unlikely to replace EBU/EDC models Detailed/Reduced kinetics with transported PDF CMC interesting for non-premixed combustion, but not making great inroads as yet

26 Future Direction 2 Radiation models Discrete Transfer Model and Discrete Ordinate Model are sufficiently sophisticated in many situations Monte Carlo method is applicable to all scenarios

27 Issues to Consider Characterisation of the source term Parameter values Boundary conditions Representing atmospheric conditions Stable Unstable Long transient simulations Experimental data Lack of data Uncertainty re. what was actually measured Ambient and initial conditions

28 Other Remedies 1 Moore s Law doubling of the number or transistors on a microchip every 2 years Constraints by physics Manufacture of chips more difficult Cooling issues Current projections are that Moore s Law will apply for another decade Parallel processing Improved chip architecture Better algorithms

29 Other Remedies 2 Adaptive Mesh Refinement and Derefinement

30 Thank You for your attention! Any Questions?

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